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Found 6 papers, 4 papers with code
Learning to Defer to a Population: A Meta-Learning Approach
The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert.
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data
Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training.
Exploiting Inferential Structure in Neural Processes
Neural Processes (NPs) are appealing due to their ability to perform fast adaptation based on a context set.
Learning the optimal state-feedback via supervised imitation learning
By substituting expert demonstrations for optimal behaviours, the same paradigm leads to the design of control policies closely approximating the optimal state-feedback.
On the stability analysis of deep neural network representations of an optimal state-feedback
Recent work have shown how the optimal state-feedback, obtained as the solution to the Hamilton-Jacobi-Bellman equations, can be approximated for several nonlinear, deterministic systems by deep neural networks.
Machine learning and evolutionary techniques in interplanetary trajectory design
After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission.
